Dynamic single-cell RNA sequencing identifies immunotherapy persister cells following PD-1 blockade
Kartik Sehgal, … , Cloud P. Paweletz, David A. Barbie
Kartik Sehgal, … , Cloud P. Paweletz, David A. Barbie
Published November 5, 2020
Citation Information: J Clin Invest. 2021;131(2):e135038. https://doi.org/10.1172/JCI135038.
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Research Article Immunology Oncology

Dynamic single-cell RNA sequencing identifies immunotherapy persister cells following PD-1 blockade

Abstract

Resistance to oncogene-targeted therapies involves discrete drug-tolerant persister cells, originally discovered through in vitro assays. Whether a similar phenomenon limits efficacy of programmed cell death 1 (PD-1) blockade is poorly understood. Here, we performed dynamic single-cell RNA-Seq of murine organotypic tumor spheroids undergoing PD-1 blockade, identifying a discrete subpopulation of immunotherapy persister cells (IPCs) that resisted CD8+ T cell–mediated killing. These cells expressed Snai1 and stem cell antigen 1 (Sca-1) and exhibited hybrid epithelial-mesenchymal features characteristic of a stem cell–like state. IPCs were expanded by IL-6 but were vulnerable to TNF-α–induced cytotoxicity, relying on baculoviral IAP repeat-containing protein 2 (Birc2) and Birc3 as survival factors. Combining PD-1 blockade with Birc2/3 antagonism in mice reduced IPCs and enhanced tumor cell killing in vivo, resulting in durable responsiveness that matched TNF cytotoxicity thresholds in vitro. Together, these data demonstrate the power of high-resolution functional ex vivo profiling to uncover fundamental mechanisms of immune escape from durable anti–PD-1 responses, while identifying IPCs as a cancer cell subpopulation targetable by specific therapeutic combinations.

Authors

Kartik Sehgal, Andrew Portell, Elena V. Ivanova, Patrick H. Lizotte, Navin R. Mahadevan, Jonathan R. Greene, Amir Vajdi, Carino Gurjao, Tyler Teceno, Luke J. Taus, Tran C. Thai, Shunsuke Kitajima, Derek Liu, Tetsuo Tani, Moataz Noureddine, Christie J. Lau, Paul T. Kirschmeier, David Liu, Marios Giannakis, Russell W. Jenkins, Prafulla C. Gokhale, Silvia Goldoni, Maria Pinzon-Ortiz, William D. Hastings, Peter S. Hammerman, Juan J. Miret, Cloud P. Paweletz, David A. Barbie

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Figure 1

Bulk RNA-Seq of MDOTS reveals a unique transcriptomic response to immune checkpoint blockade.

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Bulk RNA-Seq of MDOTS reveals a unique transcriptomic response to immune...
(A) Schematic of the workflow to profile surviving cells after treatment. αPD-1, anti–PD-1 therapy. (B) Representative immunofluorescent microscopy images of live (AO)/dead (PI) staining of MC38 MDOTS in the microfluidic devices after 6 days of treatment (scale bar: 200 μm). αCD8 = CD8 neutralizing antibody (n = 3). (C) Quantitative measurement of tumor cell viability. Data are mean ± SEM and were analyzed by multiple t tests with Bonferroni’s correction (n = 3). (D) Volcano plot of genes differentially expressed by MC38 MDOTS after 6 days of IFN-γ treatment compared with IgG isotype control. Genes with a log2(fold change) greater or less than 0.5 are shown in red. The top 5 most significant Hallmark gene sets are shown. (E) Volcano plot of genes differentially expressed by MC38 MDOTS after 6 days of αPD-1 treatment compared with IgG isotype control. Genes with a log2(fold change) greater or less than 0.5 are shown in red. The top 5 most significant Hallmark gene sets are shown. (F) Two-dimensional t-distributed stochastic neighbor embedding (t-SNE) plots of single-cell RNA-Seq (scRNA-Seq) performed on CD45/CD90+ and CD45/CD90– cells isolated from MC38 tumors at the end of in vivo treatment with either αPD-1 or IgG isotype control. t-SNE plots are colored based on treatment condition (left) and type of cells (middle). Right, t-SNE plot showing projection of bulk EMT signature (E-M) onto scRNA-Seq clusters. *P < 0.05, **P < 0.01, ***P < 0.001.